Optimizing Transfer Learning for Brain Tumor Detection: Fine-Tuning Strategies and Model Interpretability Insights

Diagnosis of brain tumors remains a significant problem in the field of neuro-oncology, especially when there are limited resources since the automated MRI analysis requires considerable time and is mostly performed manually. This study describes a more sophisticated approach to transfer learning that combines fine-tuning strategies and modern interpretability techniques to enhance the efficiency and clinical reliability of deep learning models. We assess the VGG16, ResNet50, and InceptionV3 convolutional neural networks (CNNs) using an unprecedented dataset of 24,599 MRI scans, which exceeds previous efforts in scope and variety. The technology improves model and interpretability through adaptive learning rate scheduling, layer-wise fine-tuning, and gradient-weighted class activation mapping (Grad-CAM). The experiments show great performance on benchmark datasets with an accuracy of 99.5%, while Grad-CAM images allow interpretable tumor localization and ensure essential understanding. This study addresses dataset limitations, enhance explainability for a more comprehensive scalable solution for early detection of brain tumors, increasing the role of AI in neuroimaging and improving healthcare delivery with low resources in radiology.